Monitoring network traffic is extremely important for businesses, as well as smaller organizations and personal use to ensure network performance, availability, and security. Network traffic monitoring can include reviewing incoming and outgoing data packets for possible event occurrences that affect network functioning, such as security breaches and performance bottlenecks, which can slow the network.
Conventional methods for monitoring network traffic exist for enterprise business environments, small business environments, and personal use in some cases. Such methods often create test network traffic for the development and validation of network security detection capabilities. However, conventional network traffic monitoring techniques can introduce issues into how detection is developed and tested since modern detection is moving towards machine learning. In some cases, effective testing on a live network may rely on live production nodes, which is generally undesirable due to placing the live network at actual risk.
Therefore, there is a need for an approach to generating test network traffic for the development and validation of efficient and accurate network security detection.